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Associations between nurse education and experience and the risk of mortality and adverse events in acute care hospitals: A systematic review of observational studies

2018· review· en· W2784779162 on OpenAlexafffund
Li‐Anne Audet, Patricia Bourgault, Christian M. Rochefort

Bibliographic record

VenueInternational Journal of Nursing Studies · 2018
Typereview
Languageen
FieldHealth Professions
TopicPatient Safety and Medication Errors
Canadian institutionsHôpital Charles-Le MoyneUniversité de SherbrookeCentre Hospitalier Universitaire de Sherbrooke
FundersCanadian Institutes of Health Research
KeywordsCINAHLMedicineMEDLINEObservational studyScopusAdverse effectAcute careFamily medicineSystematic reviewHealth careEmergency medicineNursingPsychological interventionInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVES: To provide knowledge from the summarization of the evidence on the: a) associations between nurse education and experience and the occurrence of mortality and adverse events in acute care hospitals, and; b) benefits to patients and organizations of the recent Institute of Medicine's recommendation that 80% of registered nurses should be educated at the baccalaureate degree by 2020. DATA SOURCES: A systematic search of English and French literature was conducted in six electronic databases: 1) Medline, 2) PubMed, 3) CINAHL, 4) Scopus, 5) Campbell, and 6) Cochrane databases. Additional studies were identified by searching bibliographies, prior reviews, and by contacting authors. REVIEW METHOD: Studies were included if they: a) were published between January 1996 and August 2017; b) were based on a quantitative research design; c) examined the associations between registered nurse education or experience and at least one independently measured adverse event, and; d) were conducted in an adult acute care setting. Data were independently extracted, analysed, and synthesized by two authors and discrepancies were resolved by consensus. The methodological heterogeneity of the reviewed studies precluded the use of meta-analysis techniques. However, the methodological quality of each study was assessed using the STROBE criteria. FINDINGS: Among 2109 retrieved articles, 27 studies (24 cross-sectional and three longitudinal studies) met our inclusion criteria. These studies examined 18 distinct adverse events, with mortality and failure to rescue being the most frequently investigated events. Overall, higher levels of education were associated with lower risks of failure to rescue and mortality in 75% and 61.1% of the reviewed studies pertaining to these adverse events, respectively. Nurse education was inconsistently related to the occurrence of the other events, which were the focus of only a small number of studies. Only one study examined the 80% threshold proposed by the Institute of Medicine and found evidence that it is associated with lower odds of hospital readmission and shorter lengths of stay, but unrelated to mortality. Nurse experience was inconsistently related to adverse event occurrence. CONCLUSION: While evidence suggests that higher nurse education is associated with lower risks of mortality and failure to rescue, longitudinal studies are needed to better ascertain these associations and determine the specific thresholds that minimize risks. Further studies are needed to better document the association of nurse education and experience with other nursing-sensitive adverse events, as well as the benefits to patients and organizations of the Institute of Medicine's recommendation.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.023
metaresearch head score (Gemma)0.094
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.023
Threshold uncertainty score0.122

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0230.094
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0080.010
Bibliometrics0.0110.014
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.317
GPT teacher head0.602
Teacher spread0.285 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations156
Published2018
Admission routes2
Has abstractyes

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